




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__			:    Any			     =      logging.get_logger(__name__)


def     lowercase		(  _lowerCAmelCase   ):
  UpperCAmelCase__         = YolosConfig()

  # size of the architecture
  if "yolos_ti" in yolos_name:
    UpperCAmelCase__         = 192
    UpperCAmelCase__         = 768
    UpperCAmelCase__         = 12
    UpperCAmelCase__         = 3
    UpperCAmelCase__         = [800, 1333]
    UpperCAmelCase__         = False
  elif yolos_name == "yolos_s_dWr":
    UpperCAmelCase__         = 330
    UpperCAmelCase__         = 14
    UpperCAmelCase__         = 6
    UpperCAmelCase__         = 1320
  elif "yolos_s" in yolos_name:
    UpperCAmelCase__         = 384
    UpperCAmelCase__         = 1536
    UpperCAmelCase__         = 12
    UpperCAmelCase__         = 6
  elif "yolos_b" in yolos_name:
    UpperCAmelCase__         = [800, 1344]

  UpperCAmelCase__         = 91
  UpperCAmelCase__         = """huggingface/label-files"""
  UpperCAmelCase__         = """coco-detection-id2label.json"""
  UpperCAmelCase__         = json.load(open(hf_hub_download(UpperCamelCase__ ,  UpperCamelCase__ ,  repo_type="""dataset"""   ) ,  """r"""   )   )
  UpperCAmelCase__         = {int(UpperCamelCase__   ): v for k, v in idalabel.items()}
  UpperCAmelCase__         = idalabel
  UpperCAmelCase__         = {v: k for k, v in idalabel.items()}

  return config


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase = False   ):
  for i in range(config.num_hidden_layers   ):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    UpperCAmelCase__         = state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''   )
    UpperCAmelCase__         = state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''   )
    # next, add query, keys and values (in that order) to the state dict
    UpperCAmelCase__         = in_proj_weight[: config.hidden_size, :]
    UpperCAmelCase__         = in_proj_bias[: config.hidden_size]
    UpperCAmelCase__         = in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    UpperCAmelCase__         = in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    UpperCAmelCase__         = in_proj_weight[-config.hidden_size :, :]
    UpperCAmelCase__         = in_proj_bias[-config.hidden_size :]


def     lowercase		(  _lowerCAmelCase   ):
  if "backbone" in name:
    UpperCAmelCase__         = name.replace("""backbone""" ,  """vit"""   )
  if "cls_token" in name:
    UpperCAmelCase__         = name.replace("""cls_token""" ,  """embeddings.cls_token"""   )
  if "det_token" in name:
    UpperCAmelCase__         = name.replace("""det_token""" ,  """embeddings.detection_tokens"""   )
  if "mid_pos_embed" in name:
    UpperCAmelCase__         = name.replace("""mid_pos_embed""" ,  """encoder.mid_position_embeddings"""   )
  if "pos_embed" in name:
    UpperCAmelCase__         = name.replace("""pos_embed""" ,  """embeddings.position_embeddings"""   )
  if "patch_embed.proj" in name:
    UpperCAmelCase__         = name.replace("""patch_embed.proj""" ,  """embeddings.patch_embeddings.projection"""   )
  if "blocks" in name:
    UpperCAmelCase__         = name.replace("""blocks""" ,  """encoder.layer"""   )
  if "attn.proj" in name:
    UpperCAmelCase__         = name.replace("""attn.proj""" ,  """attention.output.dense"""   )
  if "attn" in name:
    UpperCAmelCase__         = name.replace("""attn""" ,  """attention.self"""   )
  if "norm1" in name:
    UpperCAmelCase__         = name.replace("""norm1""" ,  """layernorm_before"""   )
  if "norm2" in name:
    UpperCAmelCase__         = name.replace("""norm2""" ,  """layernorm_after"""   )
  if "mlp.fc1" in name:
    UpperCAmelCase__         = name.replace("""mlp.fc1""" ,  """intermediate.dense"""   )
  if "mlp.fc2" in name:
    UpperCAmelCase__         = name.replace("""mlp.fc2""" ,  """output.dense"""   )
  if "class_embed" in name:
    UpperCAmelCase__         = name.replace("""class_embed""" ,  """class_labels_classifier"""   )
  if "bbox_embed" in name:
    UpperCAmelCase__         = name.replace("""bbox_embed""" ,  """bbox_predictor"""   )
  if "vit.norm" in name:
    UpperCAmelCase__         = name.replace("""vit.norm""" ,  """vit.layernorm"""   )

  return name


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase   ):
  for key in orig_state_dict.copy().keys():
    UpperCAmelCase__         = orig_state_dict.pop(UpperCamelCase__   )

    if "qkv" in key:
      UpperCAmelCase__         = key.split("""."""   )
      UpperCAmelCase__         = int(key_split[2]   )
      UpperCAmelCase__         = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
      if "weight" in key:
        UpperCAmelCase__         = val[:dim, :]
        UpperCAmelCase__         = val[
            dim : dim * 2, :
        ]
        UpperCAmelCase__         = val[-dim:, :]
      else:
        UpperCAmelCase__         = val[:dim]
        UpperCAmelCase__         = val[dim : dim * 2]
        UpperCAmelCase__         = val[-dim:]
    else:
      UpperCAmelCase__         = val

  return orig_state_dict


def     lowercase		(  ):
  UpperCAmelCase__         = """http://images.cocodataset.org/val2017/000000039769.jpg"""
  UpperCAmelCase__         = Image.open(requests.get(UpperCamelCase__ ,  stream=UpperCamelCase__   ).raw   )
  return im


@torch.no_grad()
def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase = False   ):
  UpperCAmelCase__         = get_yolos_config(UpperCamelCase__   )

  # load original state_dict
  UpperCAmelCase__         = torch.load(UpperCamelCase__ ,  map_location="""cpu"""   )["""model"""]

  # load 🤗 model
  UpperCAmelCase__         = YolosForObjectDetection(UpperCamelCase__   )
  model.eval()
  UpperCAmelCase__         = convert_state_dict(UpperCamelCase__ ,  UpperCamelCase__   )
  model.load_state_dict(UpperCamelCase__   )

  # Check outputs on an image, prepared by YolosImageProcessor
  UpperCAmelCase__         = 800 if yolos_name != """yolos_ti""" else 512
  UpperCAmelCase__         = YolosImageProcessor(format="""coco_detection""" ,  size=UpperCamelCase__   )
  UpperCAmelCase__         = image_processor(images=prepare_img() ,  return_tensors="""pt"""   )
  UpperCAmelCase__         = model(**UpperCamelCase__   )
  UpperCAmelCase__    ,       UpperCAmelCase__         = outputs.logits, outputs.pred_boxes

  UpperCAmelCase__    ,       UpperCAmelCase__         = None, None
  if yolos_name == "yolos_ti":
    UpperCAmelCase__         = torch.tensor(
        [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]   )
    UpperCAmelCase__         = torch.tensor(
        [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]   )
  elif yolos_name == "yolos_s_200_pre":
    UpperCAmelCase__         = torch.tensor(
        [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]   )
    UpperCAmelCase__         = torch.tensor(
        [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]   )
  elif yolos_name == "yolos_s_300_pre":
    UpperCAmelCase__         = torch.tensor(
        [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]   )
    UpperCAmelCase__         = torch.tensor(
        [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]   )
  elif yolos_name == "yolos_s_dWr":
    UpperCAmelCase__         = torch.tensor(
        [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]   )
    UpperCAmelCase__         = torch.tensor(
        [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]   )
  elif yolos_name == "yolos_base":
    UpperCAmelCase__         = torch.tensor(
        [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]   )
    UpperCAmelCase__         = torch.tensor(
        [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]   )
  else:
    raise ValueError(F'''Unknown yolos_name: {yolos_name}'''   )

  assert torch.allclose(logits[0, :3, :3] ,  UpperCamelCase__ ,  atol=1e-4   )
  assert torch.allclose(pred_boxes[0, :3, :3] ,  UpperCamelCase__ ,  atol=1e-4   )

  Path(UpperCamelCase__   ).mkdir(exist_ok=UpperCamelCase__   )
  print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''   )
  model.save_pretrained(UpperCamelCase__   )
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
  image_processor.save_pretrained(UpperCamelCase__   )

  if push_to_hub:
    UpperCAmelCase__         = {
        """yolos_ti""": """yolos-tiny""",
        """yolos_s_200_pre""": """yolos-small""",
        """yolos_s_300_pre""": """yolos-small-300""",
        """yolos_s_dWr""": """yolos-small-dwr""",
        """yolos_base""": """yolos-base""",
    }

    print("""Pushing to the hub..."""   )
    UpperCAmelCase__         = model_mapping[yolos_name]
    image_processor.push_to_hub(UpperCamelCase__ ,  organization="""hustvl"""   )
    model.push_to_hub(UpperCamelCase__ ,  organization="""hustvl"""   )


if __name__ == "__main__":
   snake_case__			:    Union[str, Any]			     =      argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--yolos_name''',
       default='''yolos_s_200_pre''',
       type=str,
       help=(
           '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
           ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
       ),
   )
   parser.add_argument(
       '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
   )
   parser.add_argument(
       '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
   )

   snake_case__			:    Dict			     =      parser.parse_args()
   convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase					={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["MobileBertTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileBertForMaskedLM",
      "MobileBertForMultipleChoice",
      "MobileBertForNextSentencePrediction",
      "MobileBertForPreTraining",
      "MobileBertForQuestionAnswering",
      "MobileBertForSequenceClassification",
      "MobileBertForTokenClassification",
      "MobileBertLayer",
      "MobileBertModel",
      "MobileBertPreTrainedModel",
      "load_tf_weights_in_mobilebert",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFMobileBertForMaskedLM",
      "TFMobileBertForMultipleChoice",
      "TFMobileBertForNextSentencePrediction",
      "TFMobileBertForPreTraining",
      "TFMobileBertForQuestionAnswering",
      "TFMobileBertForSequenceClassification",
      "TFMobileBertForTokenClassification",
      "TFMobileBertMainLayer",
      "TFMobileBertModel",
      "TFMobileBertPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_mobilebert import (
      MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileBertConfig,
      MobileBertOnnxConfig,
  )
  from .tokenization_mobilebert import MobileBertTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mobilebert_fast import MobileBertTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilebert import (
        MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileBertForMaskedLM,
        MobileBertForMultipleChoice,
        MobileBertForNextSentencePrediction,
        MobileBertForPreTraining,
        MobileBertForQuestionAnswering,
        MobileBertForSequenceClassification,
        MobileBertForTokenClassification,
        MobileBertLayer,
        MobileBertModel,
        MobileBertPreTrainedModel,
        load_tf_weights_in_mobilebert,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_mobilebert import (
        TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFMobileBertForMaskedLM,
        TFMobileBertForMultipleChoice,
        TFMobileBertForNextSentencePrediction,
        TFMobileBertForPreTraining,
        TFMobileBertForQuestionAnswering,
        TFMobileBertForSequenceClassification,
        TFMobileBertForTokenClassification,
        TFMobileBertMainLayer,
        TFMobileBertModel,
        TFMobileBertPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
